3D Input Convolutional Neural Networks for P300 Signal Detection

Basit Öğe Kaydı
dc.authoridORALHAN, Zeki/0000-0003-2841-6115
dc.contributor.authorOralhan, Zeki
dc.date.accessioned2025-02-24T17:18:52Z
dc.date.available2025-02-24T17:18:52Z
dc.date.issued2020
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.description.abstractP300 signal is an endogenous event related potential component. It is mostly elicited from the frontal to parietal brain lobes. Electroencephalography is used for acquiring P300 signal from scalp. P300 signal is used for brain-computer interface systems. P300 based brain-computer interface systems are preferable since they have high overall performance. The most significant overall performance indicator is information transfer rate for P300 based brain-computer interface systems. P300 signal detection accuracy and P300 detection time are using for information transfer rate calculation. Hence, P300 signal classification accuracy is important for getting higher information transfer rate. In this study, it is aimed to investigate P300 detection model for higher classification accuracy. Thus, it is proposed 3-dimensional input convolutional neural network model for P300 detection. Moreover, the proposed model was applied with region based P300 speller which constituted audio and visual stimuli. In experiments, the participants were asked to spell desired words in two sessions which were offline and online session. Linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and the proposed method were compared in both online and offline sessions. It is reached highest average classification accuracy rate with the proposed method in both sessions. According to the online session result, average classification accuracy was 94.22% in 3-dimensional input convolutional neural network model. Furthermore, average information transfer rate was 5.53 bit/min in 3-dimensional input convolutional neural network model. We have also applied methods on BCI competition III-dataset II for 2 participants A and B for evaluating performance of algorithms. The proposed method had higher classification accuracy rate than linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and multi-classifier convolutional neural network which was used in other study on same dataset.
dc.identifier.doi10.1109/ACCESS.2020.2968360
dc.identifier.endpage19529
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85079800569
dc.identifier.scopusqualityQ1
dc.identifier.startpage19521
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.2968360
dc.identifier.urihttps://hdl.handle.net/20.500.14440/871
dc.identifier.volume8
dc.identifier.wosWOS:000524758100009
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorOralhan, Zeki
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250201
dc.subjectBrain computer interface
dc.subjectdeep Learning
dc.subjecthuman machine systems
dc.subjectP300 detection
dc.title3D Input Convolutional Neural Networks for P300 Signal Detection
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu